Acoustic signals are derived from sensing the vibrations of a medium or object with an appropriate transducer. They may be recorded using microphones, hydrophones, or pressure sensors, or sensed indirectly through RF or laser vibrometry, optical interference, moving encoders, magnetic coils, or various other means. Acoustic signals typically comprise a one-dimensional time-varying signal that may be represented as a function, a voltage, a current, a pressure, a position, or so on, or may be sampled into a vector of digital numbers. Acoustic signals may also be grouped into multi-channel systems, which arise, for example, when multiple transducers are arranged to record the same signals using sensor arrays.
A “signature” is a pattern within a signal or data stream that can be associated with a condition of interest in the signal generating system. There are a host of applications for acoustic signature detection and discrimination. Acoustic signals may be indicative of a state of affairs: A failing transmission, a knocking motor, an irregular heartbeat, congested lungs, rainy or windy weather, the strain on a suspension mechanism, and the proximity of talking people are a few examples. Acoustic signals may also indicate the occurrence of an event: Breaking window glass, a roll of thunder, the report of a gunshot, the passing of footsteps, the failure of a bearing, etc. Acoustic signals may also be used to identify their source: Mr. Jones' voice, an passing tank, a terrorist's getaway vehicle, an electric fan, a French horn, the loading of a 9 mm pistol, a supersonic jet, a robin bird call, etc. The signal may be mapped to a class identity (e.g. is it a truck or a car?) or to a unique identity (e.g. is it Mr. Jones' car or Ms. Smith's car?). Acoustic signals may also be utilized to identify the location of the source. Acoustic signals of interest will arise in many fields and numerous specific examples will be obvious to one skilled in a particular field.
The human auditory system is extraordinarily good at this sort of detection and discrimination. Yet creating a reliable automated equivalent remains a challenge. A useable method must address several related goals: the signals must be translated into a representation that allows their manipulation and comparison; classes of signals must be compared in order to ascertain and extract characteristic signatures; a detector/classifier must be created to recognize signatures in a way that is robust to noise and environmental issues; and detected signatures must be localized in space. The GAD Application discloses a suite of methods that can accomplish these goals when embodied in the proper context.
In the present specification the abbreviations “GAD” refers collectively to the Greedy Adaptive Discrimination methods disclosed in the GAD Application. GAD comprises several aspects, including a Simultaneous Sparse Approximation (“SSA”) algorithm referred to herein as the “GAD decomposition algorithm” or “GAD SSA”, together with a system of signal representation and methods of processing that are reintroduced introduced as required in the text below. For clarity of description herein, the operational elements of the GAD Application are embodied in a separable module, referred to as a “GAD Engine”, which can be utilized in various aspects to achieve signature processing.
“Sparse Approximation” is a term of art that refers to representing a potentially complex signal as the sum of a relatively small collection of component elements. “Simultaneous Sparse Approximation” is therefore the representation of each member of a group of signals in terms of a common, relatively small, collection of component elements. As disclosed in the GAD Application, the GAD decomposition algorithm in some embodiments permits the common collection of component elements to be similar rather than absolutely identical, thus increasing the utility of the idea. SSA as used herein includes all variations of GAD SSA contemplated by the GAD Application as well as any similar or equivalent decomposition methods that may arise in the art.
The present invention defines certain embodiments of GAD that are applicable to acoustic signal analysis, along with certain refinements and additional complimentary methods that may be utilized in building deployable acoustic sensors and processors. The methods and embodiments will also be useful in other applications in which similar needs arise.